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The increased spatial and spectral resolution of hyperspectral images (HSIs) leads to very high data rates which causes difficulties in storing and transmitting all acquired data. Efficient compression techniques are desirable solution to this problem. In this paper, we propose a compression method based on the double sparsity model which has great abilities in capturing distinctive characters of...
In this paper, we present an efficient compression algorithm for hyperspectral images. The last decade has seen a growing interest in the study of dictionary learning and sparse representation, which have been proved to perform well on natural image compression. Because of the special techniques of hyperspectral imaging, hyperspectral images have some distinct properties when compared with natural...
The different reflection or absorption characteristics of ground targets at different wavelength lead to diverse energy distribution among band images of hyperspectral images (HSIs). In this paper, we propose a sparse representation of HSIs using learned multi-scale dictionaries with the help of the distinctive physical characteristics. First, each band is cut into patches of different size using...
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